Neural Nets
A neural net handles information in a highly parallel and distributed manner. The neural net is functionally comprised of a plurality of elementary processing units, called neurons, that are interlinked through weighted interconnections, called synapses, thus forming a massively interconnected architecture. Each synapse scales the signal supplied by a neuron acting as a source and transfers the scaled or weighted signal to a further neuron acting as a destination. Each neuron typically receives a plurality of weighted input signals either via the synapses from other neurons or from a neural net input. In a conventional neural net, each neuron sums the weighted input signals and applies a non-linear function to the sum, thereby generating an output signal for transmission to other neurons.
Neural net operation is model-free. That is, neural net operation does not require a pre-specified algorithm to perform a given task. Instead, a neural net adaptively handles data while learning from examples. Processing in a neural net is achieved in a collective manner. The simple, simultaneous operation of individual neurons and the distribution of signals throughout the neural net result in the sophisticated functioning of the neural net as a whole. This kind of organization enables a plurality of neurons to collectively and simultaneously influence the state of an individual neuron according to the application of simple rules. The expression "feed-forward neural net" refers to the arrangement of neurons in a plurality of successive layers, wherein a neuron of a particular layer supplies its output signal to another neuron in the next layer. Such a neural net can be trained, e.g., by error-backpropagation, to carry out a specific processing task. The processing capacity acquired through learning is encoded in the plurality of synapses rather than in individual memory elements. A neural net is typically suitable for handling optimization problems, carrying out pattern recognition and performing classification tasks.
Simulator using neural net
European Patent Application EP-A 0 540 168 describes a simulator that serves to simulate an object to be controlled, such as an actual machine, and that includes a neural net. A model that represents properties of the machine is conventionally prepared in advance for a known type of simulator. In order to deal with discrepancies between the designed properties that govern the model and the machine's measured actual properties, a neural network is added to correct the known simulator's results for these discrepancies. The model's output is compared to the actual output of the machine and the neural net's operational parameters are adjusted to minimize the discrepancy. This increases the accuracy of the model. In a first embodiment, the neural net in the known simulator is preceded by a differentiating means for successively differentiating a signal to be processed and for supplying the signal value and the values of its first-order and higher-order time derivatives in parallel to the neural net. In a second embodiment, the neural net is preceded by a delay line for in parallel supplying signal values associated with successive moments in time to the neural net.
The classical approach to create a suitably compact model of a dynamic system is by forging the available knowledge about the dynamic system into a numerically well-behaving hand-crafted model. It may require several man-years and a great deal of knowledge and intuition to develop a satisfactory model for, e.g., a transistor device, an economic system, weather forecasting, turning modelling into an art on its own. Due to the complications met when trying to model a dynamic system for the exploration and explanation of the underlying principles onto which the model should be based, known modelling techniques may typically resort to curve-fitting in order to obtain the necessary accuracy and smoothness. This, however, introduces discretization and interpolation effects, which may well lead to model behaviour that deviates considerably from the dynamic system it is supposed to represent. More importantly, such an approach is useful in static or quasi-static modelling only. A fully dynamical model, i.e., one that reliably predicts a time-dependent response for any type of stimulus, is practically impossible to construct on the basis of curve-fitting and interpolation techniques alone, owing to the vast amount of samples required to provide a basis for creating any set of sample values possible in the stimulus signal.